International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 11 | Nov 2023
p-ISSN: 2395-0072
www.irjet.net
STOCKSENTIX: A MACHINE LEARNING APPROACH TO STOCKMARKET Sahil Chaudhari1, Mayuresh Dhanawade2, Anay Aher3 , Rakesh Singh4 , Prof. Anjalidevi Patil5 Department of Computer Science & Engineering AI & IOT, SMT Indira Gandhi College of Engineering --------------------------------------------------------------------------***-----------------------------------------------------------------------exploratory data analysis (EDA) to gain deeper insights into Abstract: In today's information-driven financial world, timely access to and analysis of stock market news is critical for informed decision-making. This research paper presents a comprehensive approach to collecting and analyzing stock market news articles through web scraping with Beautiful Soup. We then employed Pandas to preprocess and structure the data for sentiment analysis, and Matplotlib was utilized to create visual representations of sentiment trends. The project aims to provide investors and traders with valuable insights into market sentiment for better decision-making.
the dataset.
4. Data Visualization Using Matplotlib:
- Sentiment analysis is most effective when it is presented in an easily interpretable format. Matplotlib, a widely used data visualization library, was employed to create visual representations of sentiment trends. These visualizations provide a clear and concise way to identify sentiment patterns and trends within the stock market news. we used interactive visualization tools like Plotly for more engaging and interactive sentiment trend representations.
1. Introduction:
5. Sentiment Analysis:
The financial markets operate in a dynamic and rapidly changing environment where real- time access to information can make or break an investment. Sentiment analysis of stock market news has emerged as a valuable tool for understanding market sentiment and predicting market trends. This research project outlines a novel approach to gathering, processing, and analyzing stock market news articles. It utilizes web scraping techniques, data preprocessing with Pandas, sentiment analysis, and data visualization with Matplotlib.
- Sentiment analysis was performed to assess the emotional tone and polarity of each news article. Sentiment was classified into three categories: positive, negative, and neutral. The sentiment scoring was accomplished using a predefined lexicon, machine learning models, or hybrid approaches depending on the specific use case. Sentiment analysis is a sophisticated natural language processing (NLP) technique that entails the use of computational algorithms and machine learning models to discern, classify, and quantify the subjective sentiments expressed within textual data. We have incorporated deep learning techniques, such as LSTM networks, for improved sentiment analysis performance.
2. Web Scraping Using BeautifulSoup: Web scraping is the process of extracting data from websites. We employed BeautifulSoup, a Python library, to scrape stock market news articles from various online sources. By sending HTTP requests and parsing HTML content, we collected textual data for analysis. We have also applied advanced scraping techniques such as handling AJAX requests and implementing anti-bot measures for robust data retrieval.
3. Data Preprocessing Using Pandas:
- Data preprocessing is a crucial step to ensure the quality and consistency of data. We used Pandas, a powerful data manipulation library in Python, to clean, structure, and format the collected data. Data preprocessing tasks included handling missing values, encoding text data, and ensuring data integrity. Moreover, we employed data imputation methods and conducted © 2023, IRJET
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Text Representation: Raw text data is transformed into a format suitable for analysis. This often involves techniques like tokenization (breaking text into individual words or tokens), stemming (reducing words to their base or root form), and vectorization (converting text into numerical vectors). We also explored word embedding techniques like Word2Vec to capture semantic relationships in the text. Feature Extraction: Relevant features are extracted from the text. This step identifies key elements, such as words or phrases, that contribute to the sentiment expressed. Feature extraction is crucial for training machine learning models. experimented with feature selection techniques to optimize model performance and reduce dimensionality.
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